1. Field of the Invention
The present invention generally relates to the characterization of passive sonar signals and, more particularly, to a feedforward neural network system that uses texture to automatically detect and characterize Type-I sonar signals.
2. Related Art
The Type I Signal Recognition Problem
The sounds of the ocean, captured by passive and active sonar listening devices, are often of interest. Active sonar (so und! n avigation! a nd! r anging!) listening devices are used to transmit sound waves through water and to receive the signals reflected back from objects in water. The reflected signals are analyzed to extract information regarding such objects. The reflected signals often reveal important information, such as the presence and location of vessels. Passive sonar listening devices only listen to sound (or signals) emitted from objects (e.g., vessels).
Of particular interest is a signal referred to as "Type-I." A Type-I signal refers to a specific sonar signal with a characteristic spectrogram texture. To the untrained eye, the recognition of Type-I energy is difficult; for the trained eye, the discrimination from clutter is still non-trivial, often aided by the use of high-level contextual information not typically captured by current automated processing systems. Fortunately, Type-I signals have exploitable characteristics (some of which were discovered by the inventors) that provide a reliable means to automatically extract and recognize these signals. Some of these exploitable features include:
the presence of a wideband energy component, which can be characterized by its center frequency, bandwidth, and signal-to-noise ratio (SNR); PA1 the presence--in most cases--of a narrowband energy component whose frequency is within or in close proximity to that of the wideband energy, which also can be characterized by its frequency, bandwidth, and SNR; PA1 the frequent presence of a striated pattern or texture on the signal's spectrogram image.
Having identified these features, a number of technical hurdles still exist. The relationships that must be formed from these features in order to discriminate Type-I signals from confusable clutter are complex and non-linear. Compounding the problem is the presence of interfering noise, such as biologics or noise created by a seismic profiler. The noise can obscure the texture of the Type-I signal, or in the case of Jezmonster signals (i.e., noise caused by a school of male finback whales), bury it completely.
Other schemes are available to identify Type-I signals, but they have not produced adequate results. Standard normalization line tracking schemes are available, but do not produce adequate results on wideband signals like Type I. Most standard normalization schemes suppress wideband signals, and most line extractors are optimized to track narrowband signals only. It is clear, therefore, that the complexity of the problem of detecting and characterizing Type-I signals demands a novel, integrated approach.
Neural Networks
Neural networks (NNs) have emerged as powerful tools for automatic pattern recognition tasks, especially those for which conventional methods fall far short of human performance. A neural network is a computing system made up of many simple, interconnected processing elements. It processes information by its dynamic state response to external inputs. Each processing element receives a number of inputs which are weighted according to their significance. From the weighted total input, the processing element computes a single output signal. In computing the output signal, each processing element learns, i.e., the weights on the inputs change in response to a given input and output pair.
Neural networks are suitable for automatic pattern recognition because the neural networks' ability to "learn" makes them ideal for handling nonlinear problems, which often involve data that are noisy or imprecise. But while theory suggests the broad utility of neural networks, the design of the network remains a difficult task. Many variables must be considered, such as defining the network topology and discovering the optimal set of weights for the network topology. The chosen network topology must hold sufficient complexity to address the given classification task, and the optimization algorithm used to compute the weights must be able to discover the appropriate parameters.
The difficulty of designing a suitable neural network is discussed in the article Barton et al., "Calibrating the Performance of Neural Networks," Proceedings of the IEEE Conference on Neural Networks for Ocean Engineering, Aug. 15-17, 1991. The paper offers a procedure to assess the performance of neural network classifiers. An example is described wherein three neural classifiers are tested in their ability to discriminate between a modeled underwater man-made event, real clutter signals, and a modeled quiet ocean background. Without describing the specific topology of the neural networks, the paper discusses a number of important considerations in ascertaining the performance of neural classifiers over a set of test data. For example, the performance of the network will depend on the correct construction of models of the signals to be classified, including the signals of interest (e.g., underwater man-made event), background noise, and confusable clutter. Further, networks designed to exploit the statistical information regarding the signals, such as their probabilities of occurrence, may improve the performance of the classifier. The problem remains, however, as to how to best construct the model of the signals and how to formulate the statistics of the signals.
A neural network used for the recognition of SONAR signals using shape is described in the Russo, A. P., "Constrained Neural Networks for Recognition of Passive Sonar Signals Using Shape," Proceedings of the IEEE Conference on Neural Networks in Ocean Engineering, Washington, D.C., (1991), pp. 69-76). The system processes the captured sonar signal by first deriving the spectrogram of the signal. A spectrogram is a display of the frequency content of a signal as a function of time. The various regions of energy on the spectrogram are coded using vectors that trace the regions' outer edges. The vector codes are fed into a neural network classifier which classifies the regions in terms of their distinctive frequency tracks, or more generally, by their shape.
The neural network classifier for shape consists of a bank of three similar but independent feedforward neural networks, each trained separately but with the same training set. Each network in the bank is highly structured--locally connected and greatly constrained. The bank is designed to capture three levels of information from the input pattern--global features, medium size features, and smaller features--while being able to recognize these features equally well regardless of where they occur in the input pattern. The ensemble classifies each pattern as either one of six shapes (e.g., oscillation, C-shape, pos-curve, neg-curve, etc.) or as an "unknown."
The neural network designed to classify signals using shape proves to be useful for distinguishing the frequency tracks within the signals. However, the classifier does not identify the ultimate source of the signals. In other words, while the classifier can specify that a received signal is of "C-shape," it cannot specify what source emitted the "C-shape" signal. A system and method for classifying the source of the received sonar signal is therefore required.